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career in data science

Why choose a career in Data Science? 7 reasons it is booming

The demand for data scientists won’t let up anytime soon. Why is this happening? What’s driving such a surge in demand? In this post, we’ll explore the reasons why a career in data science looks poised to remain one of the most promising careers for years to come. Data Science is booming. And if you’re wondering why, read on to discover the 7 reasons it is one of the most promising career paths available to graduates today.

Data science is a new and developing field that incorporates computer skills with analytical abilities and knowledge of statistics. A career in data science enables individuals to apply their education, work experience, and skills in order to harness the shocking amounts of information collected and mined from multiple sources on a daily basis. This has led many to suggest that today, those with expertise in data science are well poised for success.

With data science careers emerging and gaining more popularity in recent years, aspiring data scientists can wonder “should I be a Data Scientist?” This article explores 7 reasons why you should choose a career in Data Science.

A career in data science offers job security

Imagine a career that affords you job security and the opportunity to work with cutting-edge technology on a daily basis. In today’s economy, there are few careers that can make such bold promises. Many professions have become obsolete due to technological advancements; however, the field of data science is booming with demand—and it will continue to grow at an explosive rate.

One reason why this is true is that data scientists can apply their skills across many industries and sectors. Whether you’re interested in working in healthcare, government or business consulting, there are plenty of opportunities available for you! Additionally, companies want their employees to stay current on emerging technologies so they can better serve customers’ needs; therefore most employers provide training opportunities for workers who want them (which means more money).

Another benefit: Unlike other industries where employees must leave their company after 5 years before receiving retirement benefits like 401(k), companies typically offer great retirement plans within 3 years which means less stress about saving enough money for retirement later on down the road when income may not be as high anymore…

There is a high demand for data scientists

As you can see there is a high demand for data scientists.

The Bureau of Labor Statistics reports that job openings are increasing, salaries are high and there is a shortage of qualified people.

According to TechRepublic: “Data science has become one of the most sought-after career paths in tech. As an industry leader for a number of years now, it’s easy to understand why: Data science pays well, offers many job opportunities at top companies worldwide, and allows professionals to work on challenging problems that impact real people.”

Data Science helps you understand the world around you better

Data Science is a key tool for understanding the world around you. Of course, we can observe many things in the world and report on them, but we do not really understand what they mean. Data science helps us make sense of observations by providing context that can be used to draw conclusions or generate insights. Data science is also critical for making predictions or creating models that are useful in understanding cause-and-effect relationships between variables (variables are measurable quantities).

Data Science seeks to answer questions about why things happen and how they influence each other by drawing from existing experience and applying it to new situations. For example, if your business has grown over time because of a particular marketing campaign you ran last year but this year sales have dropped off slightly compared with last year—you would need data science tools to determine what caused this dropoff so that you can take steps now (rather than later) to re-create your success from last year!

Helping others is one of the reasons to choose a career in Data Science

Data science is a great way to help others. Data science can be used to fight diseases, help with education and improve business processes. For example, it has been used in healthcare to predict the risk of heart disease based on a patient’s lifestyle. It has also been used in the field of education by evaluating teachers’ performance by looking at their students’ grades and other information related to their teaching abilities.

Data scientists need empathy and social responsibility in order to succeed in this job role successfully. They should have strong analytical skills so that they can find solutions for different problems efficiently; as well as technical skills such as coding, data modeling etc. so that they can use these solutions effectively when solving real-life problems faced by various companies worldwide today.”

Data Science is also about having fun!

In addition to being a lucrative career choice, Data Science is also about having fun. While the field has its share of technical challenges and rigorous learning curves, it’s important to remember that Data Scientists are not just dealing with numbers all day. In fact, there are many aspects of the job that involve exploring new ideas and finding new ways to understand the world around us. One great example is using data science as a tool for social good. In this case, you’ll use your skills in statistics and computer science to help organizations like Google find solutions to some of today’s biggest problems—from homelessness and climate change to education inequality and more!

Another way data science can be used for social good is through civic hacking—the practice of using technology as an instrument for improving community life. Civic hackers may work directly with local governments or citizens groups on projects related to civic engagement such as election monitoring or disaster response coordination; however, no matter what type of problem they’re solving there will always be plenty of opportunity for creativity when it comes down from thinking outside traditional approaches.”

Explore various roles in Data Science

As data becomes more accessible and more people have access to powerful computing systems, there has been an increase in demand for professionals who can turn raw data into actionable insights. This demand has fueled the growth of companies that specialize in collecting and analyzing large amounts of information, such as Google or Facebook. These companies employ teams of data scientists whose job is to analyze this information along with other internal business data sources like customer purchase histories or employee performance reviews.

Some of the key skills of a data scientist include:

  • Strong communication skills – Data scientists have to work closely with other teams within an organization to understand their needs and come up with solutions that meet those needs. They also need strong analytical skills so they can interpret complex information and translate it into actionable insights for business decision-makers who don’t have extensive technical knowledge of big data or computer science principles.
  • Expertise in multiple programming languages – Data scientists are expected to know one or more high-level programming languages such as Python or R; having experience with SQL databases like PostgreSQL will help you learn faster as well as get ahead in your career by getting hands-on experience working with structured datasets right away. To learn these skills, you need a fully practical Data Science Course to understand everything practically.

Data Scientist

Data scientist is the most common job role in data science. They’re responsible for researching and analyzing large data sets, producing reports and visualizations, applying statistical models to analyze the data, and making recommendations based on that analysis.

Business Analyst

Business Analysts are a core part of any Data Science team. Their role is to gather requirements from the business, understand their problems, and then analyze the data that they have to solve these problems. They create dashboards and reports that support business decisions by analyzing data sets, visualizing them in different ways, and then mining for insights. In addition, they also work on data cleansing (making sure each variable has only one value), data warehousing (a storage system for all your raw material), data modeling (building predictive models based on historical sales or customer behaviour), and integration between different systems such as CRM systems or ERP systems with databases like Hadoop/S3

Research Scientist

Research scientists are responsible for analyzing data and creating algorithms to help understand data better. They also create new data science products, test them and improve them. Research scientists have a say in the first version of a product that’s developed by the company.

Data Engineer

A data engineer is responsible for designing, building, and maintaining data pipelines. They’re expected to be familiar with the various stages of data preparation (cleansing and checking), storage, analytics, and visualization.

They’re also responsible for engineering large-scale data infrastructures such as Hadoop clusters or Spark clusters based on customer specifications. Data engineers need to monitor the performance of these systems continuously and tune them when necessary.

They will design, implement, and maintain data models such as SQL databases or NoSQL databases that suit their customer’s needs – here’s where your knowledge of RDBMS (relational database management systems) comes in handy! They may also implement ETL (extract-transform-load) processes to move datasets between different databases depending on their contents or usage patterns across the business unit(s).

Database Administrator

You’ve probably heard of the term “database administrator,” or DBA. The role of a DBA is to ensure that databases are running smoothly, backed up properly, and able to handle any changes or issues that arise. DBAs are typically responsible for:

  • Maintaining system performance by optimizing and tuning the database
  • Backing up data and making sure it’s recoverable in case of a disaster
  • Ensuring security on both the physical level (i.e., firewalls) and logical level (i.e., permissions)

Big Data Engineer

A Big Data Engineer is a professional who has expertise in designing, building, installing, testing, and maintaining Big Data solutions. In this role, you’ll build the data pipelines that are critical to any organization’s ability to collect and analyze large volumes of information. You’ll also deploy these solutions in the cloud and monitor their performance so they run smoothly at all times. As your company grows, you’ll be responsible for scaling up your systems as well as developing new tools for managing them effectively (e.g., Hadoop). Finally, you’ll ensure that the data collected by these solutions remain secure from unauthorized access or misuse by analyzing access requests made by users on a regular basis.

Statistician

As a statistician, you would collect and analyze data from surveys, experiments, and observational studies. You would work with researchers to find meaningful patterns in the data. If necessary, you may also create methods for analyzing data sets. You may also help develop a statistical method or model, but this is typically not your primary responsibility.

Statisticians often work for government agencies, private companies, or universities. They may create reports using the results of statistical analyses or present their findings to individuals who don’t have any experience in statistics at all!

Machine Learning Engineer

As the field of data science grows, there are a number of different roles that professionals can take on. One such role is that of a machine learning engineer. This job requires in-demand skills, and it’s important to know how to become one if you’re interested in pursuing it.

Machine Learning Engineers

As the name implies, machine learning engineers are responsible for building systems that leverage machine learning algorithms to make predictions and automate processes. They use their technical expertise and knowledge about data science tools like Hadoop or Spark to build these types of systems for clients. A large part of their work involves building these systems using Python programming language because Python allows them to prototype faster than most other languages do (which means they get results faster).

Starting salary in Data Science in India

The range of salaries for an entry-level data scientist in India is between ₹5 LPA and ₹12 LPA (10,000$ to 24,000$). The mean salary is ₹7.8 LPA (11,333$). The median salary is ₹8.5 LPA (12,500$), whereas the mode is 6LPA.

The standard deviation is 2.1; i.e., there are many people who earn more than 12k and less than 10k as well!

A fresher with a post-graduation degree in data science can look forward to a starting salary of approximately Rs. 10,000 to Rs. 15,000 per month.

The average starting salary for a data scientist in India is ₹5 to ₹12 LPA (₹5 lakhs per annum). However, the average starting salary for an entry-level data scientist is ₹10,000 to ₹15,000 per month.

As a data scientist, you get paid based on your experience. Typically, an experienced data scientist can make up to Rs. 25,000 to Rs.30, 000 per month depending upon their skills and experience. The salary also varies depending upon the factors like location of work and industry verticals where you work in India.

Once you have some experience in data science, you can expect a salary of 12 to 25 LPA.

A fresher’s starting salary will be around ₹5 LPA. As an entry-level employee, the average salary for someone with 0-6 months of experience is between ₹4 and 6 lakh per annum (LPA). This can go up to 7 LPA for those with more than 3 years of work experience under their belt.

Take advantage of free learning opportunities

  • Start with online courses. They are one of the fastest ways to get up to speed on a topic, and they offer you the flexibility to learn at your own pace. There are dozens of free resources available through sites like Coursera, Udacity, and edX that can help you develop a strong foundation in data science skills. You can also find many paid courses through platforms like Dataquest (which offers grants for women and minorities) or Galvanize (which offers job placement assistance).
  • Take advantage of online communities and support groups. If you don’t have access to a physical community where people share ideas with each other, it’s much easier to feel isolated as a developer! Luckily there are plenty of online communities where people come together over shared interests; some notable examples include Women Who Code, Data Science Vixens (for women), Women Analytics Professionals (WAPO), PyLadies NYC Meetup Group (for Python lovers), AWS Lambda Women Developers Community groups worldwide

It is a great time to pursue a career in Data Science!

It is a great time to pursue a career in Data Science. With the boom of big data and machine learning, there are many opportunities for you to explore the various roles in Data Science. You can take advantage of free learning opportunities and build your skill set through MOOCs (Massive Open Online Courses) or short courses offered by edX and Udemy.

Data science helps you understand the world around you better; it provides insights into how humans behave, what they want, and what motivates them to do things. It also allows businesses to make better decisions based on data-driven analyses.

Other reasons why choosing a career in Data Science might be right for you include being able to help others through your job or finding something that interests you such as building cool apps or creating new algorithms for fun!

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